Image recognition and computer vision has been around since at least the 1960's when universities began pioneering artificial intelligence. Early on, when attempting image recognition, challenges such as viewpoint variation, scale, orientation, deformation, occlusion, illumination condition, background clutter, intra-class variation, object detection, and the like, emerged. Although improvements and advances have been made in these image recognition challenges over the last 50 years, current methodologies still have difficulty efficiently and accurately identifying objects contained in images.
FIG. 1 is an example of an image which is very difficult recognize using computer vision. FIG. 1 shows a typical floorplan image 2. As can be seen from the image, a variety of room spaces of varying dimensions are represented. Both pivoting doorways and sliding closet doors are depicted. Typical symbols representing appliances and fixtures are present. As can happen, area dimensions are given in divergent measurement terms: areas 10 are described in conventional Japanese measurements units of “j” or Jo, while area 12 is described in measurement units of “m2” or meters squared.
Floorplan image 2 shows interior and exterior walls 8 and doors 6. Further, floorplan image 2 also shows bedrooms 10, balcony 12 and living room 14. As can be appreciated, image recognition and computer vision processing require great amounts of computer processing resources.